尿囊急性炎症和盆炎:使用微调的大型语言模型进行诊断
Mohammad Khaleel Sallam Ma'aitah1, Abdulkader Helwan2, Abdelrahman Radwan1
1Electrical Engineering/Robotics and Artificial Intelligence Engineering, Faculty of Engineering & Technology, Applied Science Private University, Amman 11931, Jordan.
Journal of personalized medicine
|February 25, 2025
概括
大型语言模型 (LLM) 在诊断尿路疾病方面表现有前途. 微调模型,如GPT-2和BERT,数据有限,可实现膀炎和炎的高诊断准确度.
科学领域:
- 医疗信息学医学信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在自然语言处理 (NLP) 中的文本分析和分类方面表现出色.
- 自动回归模型在文本分类任务中特别有效.
研究的目的:
- 评估基础的LLM的诊断能力,包括GPT-2,BERT,Distill-BERT和TinyBERT.
- 评估LLM在诊断急性泌尿膀炎症和骨腎炎方面的表现.
主要方法:
- 监督微调 (SFT) 应用于训练和测试选定的LLMs.
- 使用了120个示例的数据集,详细说明了目标条件的症状.
- 用精心制作的提示符来呈现用于LLM分析的数据.
主要成果:
- 在诊断这些疾病时,GPT-2和BERT实现了100%的准确性.
- 蒸BERT和TinyBERT的高精度分别为94%和79%.
- 该研究证实了使用最小训练数据以获得合理的诊断结果的可行性.
结论:
- 基础的LLM可以有效地用于诊断尿路疾病,但数据有限.
- SFT和快速工程是实现高诊断准确度的关键.
- 该研究强调了NLP模型在临床诊断中的潜力.
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